CVE-2025-1796
Langgenius Dify 0.10.1
Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2025-1796 is a high-severity PRNG (CWE-338) vulnerability in Langgenius Dify. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Forge Web Credentials (T1606); ranked at the 43th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to SC-13 (Cryptographic Protection) and SA-8 (Security and Privacy Engineering Principles) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-1796, published on 2025-03-20, is a vulnerability in langgenius/dify version 0.10.1 that stems from the use of a weak pseudo-random number generator (PRNG) for generating password reset codes. Specifically, the application employs Python's `random.randint` function, which is not cryptographically secure and is classified under CWE-338 (Use of Cryptographically Weak Pseudo-Random Number Generator). This flaw enables attackers to crack the codes, with a CVSS v3.1 base score of 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H).
The attack requires low-privileged access (PR:L), such as to workflow tools within the application, allowing network-based exploitation (AV:N) without user interaction (UI:N). An attacker can extract PRNG output from these tools to predict subsequent password reset codes, facilitating unauthorized takeover of any account, including administrator accounts, and leading to full application compromise with high impacts on confidentiality, integrity, and availability.
Details on advisories, patches, and mitigation are available in the Huntr security bounty report at https://huntr.com/bounties/a60f3039-5394-4e22-8de7-a7da9c6a6e00.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-6805
Vulnerability Data
A vulnerability in langgenius/dify v0.10.1 allows an attacker to take over any account, including administrator accounts, by exploiting a weak pseudo-random number generator (PRNG) used for generating password reset codes. The application uses `random.randint` for this purpose, which is not…
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suitable for cryptographic use and can be cracked. An attacker with access to workflow tools can extract the PRNG output and predict future password reset codes, leading to a complete compromise of the application.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: dify
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 3 hardening rules · 3 OS baselines
V7.2.3V11.5.1
Mitigating Controls (NIST 800-53 r5) AI
Requiring specific approved cryptography for protection directly mandates use of strong PRNGs instead of weak ones.
Engineering principles applied during design and development include selection of cryptographically strong random number generation.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices explicitly require cryptographically strong RNG selection and usage in security contexts.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Mandates use of approved cryptographic controls, directly requiring cryptographically strong RNGs.
Security testing can detect use of weak random number generators.
Secure SDLC processes should catch weak PRNG usage during design and code review.
Application security requirements can specify cryptographically strong random number generation.
Secure engineering principles include selection of appropriate cryptographic primitives.
Secure coding standards prohibit use of weak PRNGs in security contexts.
Hardening callouts derived
Configuration rules from DISA STIG baselines that bear on weaknesses of the type cited by this CVE. Each rule is shown with the relationship its mapping actually records, against the CWE it was authored against. Derived via CVE→CWE over `controls_xwalks` (authoritative rows only; rows rated `none` are excluded).
Oracle Linux 8 (1 rule)
- V-248563 The OL 8 SSH server must be configured to use strong entropy. prevents CWE-338
RHEL 8 (1 rule)
- V-230253 RHEL 8 must ensure the SSH server uses strong entropy. prevents CWE-338
Ubuntu 22.04 (1 rule)
- V-260650 Ubuntu 22.04 LTS must implement NIST FIPS-validated cryptography to protect classified information and for the following: To provision digital signatures, to generate cryptographic hashes, and to protect unclassified information requiring confidentiality and cryptographic protection in accordance with applicable federal laws, Executive Orders, directives, policies, regulations, and standards. prevents CWE-338